Okay, so I’ve been meaning to mess around with this “bears 2017” dataset for a while now. It sounded interesting, and I finally got around to digging into it. Here’s how it went down.
Getting Started
First things first, I needed to actually find the data. I did some Googling and eventually stumbled upon it. It wasn’t super straightforward, a little digging involved, but hey, that’s part of the fun, right?
Figuring Out the Data
Once I got my hands on the data, I opened it up. I am use Pandas to read the dataset. It looked like a bunch of rows and columns, numbers everywhere, the usual stuff. I spent some time just poking around, trying to get a feel for what each column actually meant. What’s a “bear_id”? What are these measurements?
Cleaning Things Up
Of course, the data wasn’t perfect. There were some missing values here and there, some weird outliers that didn’t make sense. So, I rolled up my sleeves and started cleaning. I decided to drop some rows, fill in some missing bits with averages – you know, the typical data cleaning dance.
Analyzing the Bears
Now for the interesting part! I started playing with the data, creating some charts and graphs. I wanted to see if there were any patterns.
- Are the bears getting bigger over time?
- Are there differences between different groups of bears?
- Do some bears have unusually long claws?
I used things like scatter plots, box plots, and even tried a couple of histograms to see the distributions.
My Findings
After all that work, I did find a few interesting trends. I noticed that the weights some bears showed the data is not integrity, and I have to clean some rows. It was pretty cool to see those trends appear from the raw data. I jotted down my observations. It’s not exactly groundbreaking research, but it was a fun little project to tackle on my own.
Conclusion: I set out to find and understand the data, I cleaned, analyzed, and figured out a few of the patterns. Success!